A New 3D Tool for Planning Plastic Surgery

Face plastic surgery (PS) plays a major role in today medicine. Both for reconstructive and cosmetic surgery, achieving harmony of facial features is an important, if not the major goal. Several systems have been proposed for presenting to patient and surgeon possible outcomes of the surgical procedure. In this paper, we present a new 3D system able to automatically suggest, for selected facial features as nose, chin, etc, shapes that aesthetically match the patient's face. The basic idea is suggesting shape changes aimed to approach similar but more harmonious faces. To this goal, our system compares the 3D scan of the patient with a database of scans of harmonious faces, excluding the feature to be corrected. Then, the corresponding features of the k most similar harmonious faces, as well as their average, are suitably pasted onto the patient's face, producing k+1 aesthetically effective surgery simulations. The system has been fully implemented and tested. To demonstrate the system, a 3D database of harmonious faces has been collected and a number of PS treatments have been simulated. The ratings of the outcomes of the simulations, provided by panels of human judges, show that the system and the underlying idea are effectiv

Automatic features characterization from 3d facial images.

This paper presents a novel and computationally fast method for automatic identification of symmetry profile from 3D facial images. The algorithm is based on the concepts of computational geometry which yield fast and accurate results. In order to detect the symmetry profile of a human face, the tip of the nose is identified first. Assuming that the symmetry plane passes through the tip of the nose, the symmetry profile is then extracted. This is undertaken by means of computing the intersection between the symmetry plane and the facial mesh, resulting in a planner curve that accurately represents the symmetry profile. Experimentation using two different 3D face databases was carried out, resulting in fast and accurate results

Planning Plastic Surgery in 3D. An innovative approach and tool

Face plastic surgery (PS) plays a major role in today medicine. Both for reconstructive and cosmetic surgery, achieving harmony of facial features is an important, if not the major goal. Several systems have been proposed for presenting to patient and surgeon possible outcomes of the surgical procedure. In this work, we present a new 3D system able to automatically suggest, for selected facial features as nose, chin, etc., shapes that aesthetically match the patient’s face. The basic idea is suggesting shape changes aimed to approach similar but more harmonious faces. To this goal, our system compares the 3D scan of the patient with a database of scans of harmonious faces, excluding the feature to be corrected. Then, the corresponding features of the k most similar harmonious faces, as well as their average, are suitably pasted onto the patient’s face, producing k+1 aesthetically effective surgery simulations. The system has been fully implemented and tested. To demonstrate the system, a 3D database of harmonious faces has been collected and a number of PS treatments have been simulated. The ratings of the outcomes of the simulations, provided by panels of human judges, show that the system and the underlying idea are effective

Hacia la determinación de la asimetría facial utilizando nubes de puntos provenientes de imágenes RGB-D

El presente trabajo de investigación tiene como objetivo general determinar la asimetría del rostro humano utilizando nubes de puntos que provienen de imágenes RGB-D. Para lograr este objetivo se implementó una metodología conformada por dos etapas: la primera consistió en generar una nube de puntos facial densa y la segunda, donde se determinó la asimetría facial de la nube de puntos. En la primera etapa se evaluaron dos plataformas para la adquisición de la imagen 3D, que pudieran operar en las dimensiones mínimas establecidas para un consultorio médico general. La primera plataforma estuvo conformada por el sensor Kinect para Xbox 360 © (Kinect ©), una computadora y el software Skanect © v 1.91 y la segunda plataforma por el sensor Sense 3D Cubify © (Sense ©), una computadora y el software Sculpt v 1.0. De las imágenes 3D se extrajo la nube de puntos con la región facial y se incrementó su densidad con interpolación de función de base radial (RBF). Con base en las nubes de puntos faciales resultantes se determinó utilizar la plataforma del sensor Kinect © en la segunda etapa de la metodología. La adquisición de las imágenes 3D se obtuvieron de los rostros de sujetos sanos que no presentaban una asimetría clínica. Para determinar su asimetría facial, se adquirieron las imágenes 3D de los rostros en reposo y con tres asimetrías simuladas. Las tres asimetrías en los rostros de los sujetos fueron simuladas por medio de una deformación que se realizó colocando tres objetos esféricos de diferentes tamaños en la mejilla derecha. Con esto se logró un incremento en las dimensiones de la mejilla del lado derecho del rostro provocando una diferencia con la mejilla del lado izquierdo. En la segunda etapa de la metodología se determinó la asimetría facial de cada una de las nubes de puntos densas del rostro en reposo y con las tres asimetrías. Se uso el plano de simetría obtenido de la superposición de la nube de puntos facial y su reflejo, y del cálculo de las distancias existentes entre ambas nubes. De las distancias resultantes se calculó la distancia media absoluta que corresponde al índice de asimetría facial. Los índices de asimetría facial obtenidos, demuestran de forma cuantitativa las diferencias entre el rostro en reposo y el rostro asimétrico cada sujeto. El índice menor correspondió al rostro en reposo y el índice mayor al del rostro con la tercera asimetría. Considerando estos resultados, se puede concluir que se logró el objetivo buscado.Conacyt - becario naciona

3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques.

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods